On the other hand, for adding geometric information, we extract the distance of pixels to objects' boundaries (i.e., distance transform [1]}).
Using this distance transform as a task learning gives us the following advantages:
i) we can easily extract it from the instance masks, and
ii) the quantized distance transform can be easily trained with the existing loss functions.
Note that this representation, based on the distance transform, allows us to infer the complete shape of an object instance even with incomplete information (i.e., when a part of the object is shown).
